Spectrometry can be a useful tool for detecting and/or quantifying analytes of interest in a medium, for example for determining a glucose concentration in blood or tissue. Several types of spectrometry are known, such as for example radio-frequency, mid-infrared, near-infrared, Raman, visual spectrum or near-ultraviolet spectroscopy. For spectrometric detection and quantification, a good signal to noise ratio is desirable, e.g. such as to be able to distinguish a weak spectral signal related to the analyte of interest from a noisy background signal, e.g. containing a complex mixture of spectra of known and/or unknown chemical compounds in a known or unknown mixture composition.
In order to detect and/or quantify an analyte of interest, a method or system known in the art may probe a sample, for example a tissue, using radiation, e.g. infrared radiation. The analyte, when present in the sample, absorbs and/or interacts with the radiation in accordance with a reference spectrum of the analyte. For example, glucose absorbs radiation at known frequencies in the near-infrared and mid-infrared spectral range. However, the background interaction of other components of the sample, e.g. other analytes present in the tissue or blood sample being tested for glucose content, such as haemoglobin, may interact at similar frequencies. Therefore, a calibration procedure may be used to relate measured spectral data to the concentration of the analyte of interest. Such calibration procedure may for example involve a multivariate analysis of reference spectra obtained for reference samples having known concentrations of the analyte of interest.
For example, in Raman spectroscopy, the vibrational, rotational, and other low-frequency modes of a system are characterized by the Raman energy spectrum generated by inelastic scattering phenomena in this system, e.g. caused by the interaction of substantially monochromatic light with molecular vibrations, phonons or other excitation modes of the system. Raman spectroscopy can be particularly useful for microscopic analysis, since sectioning or fixation of the sample is not required and the spectral data can be collected from a small volume, for example a volume of about a micrometer in diameter. Furthermore, Raman spectroscopy can be used for imaging, e.g. by parallel excitation and spectral data collection over a plurality of points distributed over a sample to be imaged or by scanning an excitation beam over the sample while collecting the spectral data as function of location. Raman spectroscopy in the near-infrared electromagnetic spectrum also offers the advantage of a low risk of damaging the sample, and the possibility of non-invasive in-vivo measurements, e.g. to detect analytes of interest in tissue and blood through the skin.
However, the spectral data obtained from Raman scattering can be quite weak, e.g. distinguishing the inelastically scattered light from other light signals, such as Rayleigh scattered light, can be difficult. For example, even though Raman spectrometry may be suitable for detecting the presence of an analyte, such as for determining a glucose concentration in blood, the Raman signal for characterizing the analyte can be very weak and difficult to separate from its noisy background. Therefore, it would be advantageous to obtain low noise levels relative to the signal intensity levels obtained by a Raman spectrometer and to provide a high quality algorithm for extracting a component of interest from the acquired spectral information, e.g. to extract the glucose signal from spectral information obtained from a blood sample. Furthermore, it would also be advantageous to achieve a high throughput while obtaining Raman spectral data, e.g. while Raman imaging, and extracting information of interest from the collected spectral data.
Integrated systems for collecting spectrometric data from samples are known in the art. For example, silicon-on-insulator arrayed waveguide spectrometers are known in the art that can comprise, for example, 50 channels. However, other prior-art integrated spectrometry devices do not require a grating. For example, U.S. Pat. No. 7,361,501 discloses a spectral analyser having one or more Mach-Zehnder interferometers, a detector and a microprocessor.
Tarumi et al., “Multivariate calibration with basis functions derived from optical filters,” Anal. Chem. 2009, 81, 2199-2207, disclosed a numerical optimization method to define a set of Gaussian basis functions that can be used to represent the important information in a calibration set of near-IR spectra. This may advantageously provide a lower-order basis. Furthermore, because of their analogy to optical filters with a single band-pass, the Gaussian basis functions allow the calibration model to take the form of a specialized filter photometer that is dedicated to a given analytical determination.